Blog Post

What TV needs: personalized search and recommendations

We’ve long believed that the future of video discovery will be based on personalized search results and recommendations, which will not only help viewers find what they’re looking for but will also offer up videos they might be interested in, based on their viewing history. Netflix (s NFLX) and others have had success with their own recommendations algorithms, but there’s not really a good way to aggregate video from all the disparate video sources available to online and even traditional video distributors. That’s where Digitalsmiths’ new Seamless Discovery technology comes in.

Until now, Digitalsmiths has mainly been known for its expertise in providing rich, time-based metadata to video providers, allowing them to index, manage and distribute videos based on the data it provides. It uses technologies like facial recognition, scene classification, object identification and closed-captioned time alignment to determine exactly who is in a scene and what is happening, and to tie that information to video on a second-by-second basis.

Now it’s taking all of what it knows about the videos that people watch to help determine what it is that they like and applying that information to create personalized search and recommendations. Its Seamless Discovery technology is being pitched to pay-TV operators, consumer electronics manufacturers and content owners as a way to quickly offer up more-relevant content to their users.

Personalized recommendations are designed to ensure that viewers get the most-relevant results based on their viewing history and can be extremely powerful in keeping users coming back. Netflix might be the best example of this: About 60 percent of its viewing happens based on content it’s recommended, not from user queues or titles they searched for. With personalized search, Digitalsmiths Seamless Discovery also provides results that are targeted based on what the user has previously watched or searched for, in the same way that Google search results differ by user when they are logged in.

Digitalsmiths hasn’t just built an algorithm based on its data set and that of its customers; it is leveraging a ton of third-party data as well. It’s also integrated with scheduling information from providers like Tribune Media Services to enable customers to apply its personalized discovery technology to linear TV as well as on-demand videos.

These recommendations are powered by a cloud-based API that customers can hook into, allowing them — the content owners, distributors and device manufacturers — to build their own presentation layer on top of it. That will give them flexibility to build the system into multiple devices and to provide a universal search for various content sources. For pay-TV providers, that can mean universal search between live TV, video-on-demand and even streaming services. Meanwhile, device manufacturers can leverage the technology as a top-layer search or discovery mechanism that would allow users to see recommended content from a number of connected TV apps.

Currently both Cisco (s csco) and Technicolor are on board (both are also investors, see below), and they plan to offer Digitalsmiths’ personalized search and recommendations technology to their clients. Digitalsmiths is also in testing with some major operators and content owners, Digitalsmiths CEO Ben Weinberger told us in a phone interview, but he declined to say who they were.

Earlier this year, Digitalsmiths raised $12.5 million in a Series C round that was led by Technicolor and included existing investors .406 Ventures, Aurora Funds, Chrysalis Ventures, Capitol Broadcasting and Cisco. That brings total funding to more than $30 million. Its customers include Warner Bros, (s TWX) Paramount, (s VIA) Technicolor, Turner, the NBA and NASCAR.

Google serves me adverts across the breadth of the web, through its numerous touch-points and technologies, based on everything it knows about me; However, it still takes at least a single trigger-action from me to be most effective, online video will be me.tv in a similar (and a margin of error of being very wrong).

Tivo has been doing something similar for some time (also, not unlike the datasets/features held by Amazon), yet nothing replaces serendipitous random discovery, the taste-makers, happenstance, and the simple fact that good stories, and classics and financing, can’t rely on nebulous or superficial concepts of “followers” and “mentions” as meaningful metrics of success.